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Presentation BSLab 2020: Reliable Signals and Limit Conditions using Trigonometric Interpolation for Algorithmic Capital Investments.

Authors:
  • Algorithm Invest

Abstract

Presentation: Reliable Signals and Limit Conditions using Trigonometric Interpolation for Algorithmic Capital Investments
Reliable Signals and Limit Conditions using
Trigonometric Interpolation
for Algorithmic Capital Investments
Cristian una
Economic Informatics Doctoral School
Academy of Economic Studies, Bucharest, Romania
Business Systems Laboratory – 7th International Symposium
SOCIO-ECONOMIC ECOSYSTEMS: CHALLENGES FOR SUSTAINABLE DEVELOPMENT IN THE DIGITAL ERA
January 22-24, 2020 University of Alicante, Spain
This paper was financed by Al gorithm Invest (algoinvest.biz)
This paper presents:
How to build the trigonometric interpolation
for time price series of any financial market.
Mathematical proof of a direct and strong correlation
of the Trigonometric Price Line and the price movement.
A method to measure the trend power using
the gradient of the Trigonometric Price Line.
Mathematical models to build automated trading signals
and limit conditions using the Trigonometric Price Line.
A precise combined investment method using Trigonometric
Price Line and Price Prediction Line.
Real investment results obtained with the presented
methods to prove the efficiency and simplicity involved.
The presented methods can be applied:
For any time
price series
For any financial
markets
For any
timeframe
To build
automated
investment
signals
To build
automated limit
conditions
To build a stable
investment
method
To improve any
other investment
strategy
To improve any
automated
investment
software
To increase the
capital efficiency
of any
investment plan
Trigonometric interpolation
Having a time price series given by points:
The interpolation function will be found as the
analytical form who minimize total error:
The Trigonometric Price Line
is defined as the interpolation obtained with the
next analytical form:
Trigonometric interpolation for different time frames
A strong and direct correlation is proved:
0.551 r0.999
for all next capitalmarkets:
DAX – Frankfurt Stock Exchange Deutscher Aktienindex
DJIA – US Wall Street Dow Jones IndustrialIndex
S&P US Standard & Poor's Market Index
NASDAQ US Nasdaq Stock Market Index
FTSE – UK Financial Times Stock Exchange
SMISwiss Stock Exchange Market Index
ASX – Australian Stock Exchange Market Index
NIKKEI Japanese Stock Exchange Nikkei Index
CAC– France Cotation Assistée en Continue Index
CURRENCIES – EURUSD, EURJPY, GBPUSD
GOLD Spot price XAUUAD, XAUEUR, XAUAUD
OIL BRENT CRUDE OIL
The trend power can be measured as:
Automatic entry investment signals can be assembled with:
Automatic exit investment signals can be assembled with:
Results for investments in DAX30 between 01.07.2016 and 30.06.2019.
Combined investment method using
Trigonometric Price Line and Price Prediction Line.
Instead of conclusions
The combined investment signals usint the Treigonometric Price Line
and the Price Prediction Line, made by the formula (14), applied for
Frankfurt Stock Exchange Deutscher Aktienindex DAX30 between
01.07.2016 and 30.06.2019 it was obtained a risk to reward ratio of:
1:4.13 (4.13€ profit for each1€ risk)
This result means 10:41.3, or 20:82.6, or 25:103.25
The conditions these results were obtained are presented in the paper.
Capital evolution due to the Trigonometric Price Line investment signals.
Reliable Signals and Limit Conditions using
Trigonometric Interpolation
for Algorithmic Capital Investments
Cristian Păuna
Email: cristian.pauna@ie.ase.ro
Phone: +407.4003.0000
Economic Informatics Doctoral School
Academy of Economic Studies, Bucharest, Romania
This paper was financed by Algorithm Invest (algoinvest.biz)
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